文件列表:

calculate_layer_smap.m (1467, 2015-10-25)
get_mdf_smap.m (966, 2015-10-25)
init_deepmodel.m (450, 2015-10-25)
mdf_demo.m (474, 2015-10-25)
mult_seg.m (1024, 2015-10-25)

The is a demo code for paper "Visual Saliency Based on Multiscale Deep Features" by Guanbin Li and Yizhou Yu, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), Boston, June, 2015. written by Guanbin Li Email:gbli@cs.hku.hk ****************************************************************************************************************** The code is tested on ubuntu14.10 with MATLAB R2014a. ****************************************************************************************************************** Usage: 1. set up caffe and check that Caffe MATLAB wrapper is set up correctly. Refer to http://caffe.berkeleyvision.org/installation.html for caffe installation. 2. run 'mdf_demo.m' Note: 1. We utilize graph based sementation method[2] and used the 15 layer decomposition parameters suggested in [3]. 2. We utilize alexnet model for feature extraction. 3. We utilize DeeplearningToolbox[4] in training our neural network model. ##Citing our work Please kindly cite our work in your publications if it helps your research: @InProceedings{Li_2015_CVPR, author = {Li, Guanbin and Yu, Yizhou}, title = {Visual Saliency Based on Multiscale Deep Features}, journal = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2015} } ##License Copyright (c) 2015, Guanbin Li All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. References: [1] Guanbin Li and Yizhou Yu, Visual Saliency Based on Multiscale Deep Features, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, June 2015. [2] Felzenszwalb, Pedro F., and Daniel P. Huttenlocher. "Efficient graph-based image segmentation." International Journal of Computer Vision 59.2 (2004): 167-181. [3] Jiang, Huaizu, et al. "Salient object detection: A discriminative regional feature integration approach." Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on. IEEE, 2013. [4] Palm, Rasmus Berg. "Prediction as a candidate for learning deep hierarchical models of data." Technical University of Denmark (2012).

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